[R] ltm: Simplified approach to bootstrapping 2PL-Models?

[Dimitris Rizopoulos]

Note that you can also fit 2PL models using the gpcm() function that 
fits the Generalized Partial Credit Model, and therefore you can use the 
GoF.gpcm() function for goodness-of-fit, e.g.,

library(ltm)

fit2PL.1 <- ltm(LSAT ~ z1)
fit2PL.2 <- gpcm(LSAT)

fit2PL.1
fit2PL.2

GoF.gpcm(fit2PL.2, B = 199)


I hope it helps.

Best,
Dimitris


On 11/20/2011 1:16 PM, Christian Montel wrote:
> Dear R-List,
>
> to assess the model fit for 2PL-models, I tried to mimic the
> bootstrap-approach chosen in the GoF.rasch()-function. Not being a
> statistician, I was wondering whether the following simplification
> (omit the "chi-squared-expressed model fit-step") would be appropriate:
>
> GoF.ltm<- function(object, B = 50, ...){
>    liFits<- list()
>    for(i in 1:B){
>      rndDat<- rmvlogis(nrow(object$X), coef(object))
>      liFits[[i]]<- ltm(rndDat ~ z1)
>    }
>    distr<- sort(sapply(liFits, function(x)return(x$log.Lik)))
>    return(max(which(distr<= object$log.Lik))/length(distr))
> }
>
> The rationale behind was to directly use the sorted sequence of
> (log)likelihoods of models fitted to 2PL-fitting-datasets where the
> parameters of<object>  hold. The return value was intented to roughly
> mirror how many 2PL-Datasets which demonstrably fit the model fit
> worse or better than the model in question.
>
> Any comments which may help me figure out whether I'm on the right
> track are greatly appreciated.
>
> Thank you in advance,
> best regards,
> Christian
>
>
> Dr. Christian Montel
> E-Mail: christian.montel at eligo.de
> eligo GmbH -- Büro Berlin		
> Tel.:       +49 (0) 30 695 399 95-2
> Arndtstr. 34				
> Fax:        +49 (0) 30 695 399 95-1
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>
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-- 
Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus University Medical Center

Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
Tel: +31/(0)10/7043478
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